Efficient Optimization of Energy Consumption at Home through Machine Learning

In recent years, the imperative need for efficient energy utilization in residential buildings has become increasingly evident due to the unwarranted wastage of electrical energy. This has spurred significant interest in optimizing energy consumption while maintaining user comfort. Accurate energy prediction is a crucial component of this optimization. This study focus on minimizing energy consumption by accurately predicting it through advanced Machine Learning (ML) models and optimization techniques. The study begins with the collection of energy data from a reliable source, Kaggle, comprising 29 features. To streamline the dataset, unnecessary features are discarded, and data normalization is performed to ensure consistency and reliability. Subsequently, ML models, specifically Long Short-Term Memory (LSTM), are designed and optimized through the Genetic Algorithm (GA) and Grey Wolf Optimization (GWO) to fine-tune hyperparameters. The prediction results are evaluated using error values to assess the accuracy and reliability of the models. Notably, our findings indicate that the GWO-LSTM model outperforms the others, exhibiting minimal errors and therefore showcasing its superior predictive capabilities. Accurate energy prediction not only serves as a valuable tool in its own right but also plays a pivotal role in enabling proactive energy management. By accurately forecasting future energy requirements, it becomes possible to optimize energy consumption further. Such precision paves the way for intelligent scheduling of home appliances, which, in turn, leads to significant reductions in energy consumption.